Added example for serialization to save data (addresses #20)

examples/Project.toml

@@ -15,4 +15,5 @@ Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 QuartzImageIO = "dca85d43-d64c-5e67-8c65-017450d5d020"
+Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"

examples/read.jl

@@ -0,0 +1,91 @@
+#!/usr/bin/env bash
+    #=
+    exec julia --project="$(realpath $(dirname $0))/" "${BASH_SOURCE[0]}" "$@" -e "include(popfirst!(ARGS))" \
+    "${BASH_SOURCE[0]}" "$@"
+    =#
+
+
+#=
+Adapted from https://github.com/Simon-Hohberg/Viola-Jones/
+=#
+
+
+println("\033[1;34m===>\033[0;38m\033[1;38m\tLoading required libraries (it will take a moment to precompile if it is your first time doing this)...\033[0;38m")
+
+include(joinpath(dirname(dirname(@__FILE__)), "src", "FaceDetection.jl"))
+
+using .FaceDetection
+const FD = FaceDetection
+using Printf: @printf
+using Images: imresize
+using Serialization: deserialize
+
+println("...done")
+
+function main(;
+    smart_choose_feats::Bool=false, alt::Bool=false
+)
+
+	# we assume that `smart_choose_feats = true`
+    main_path = dirname(dirname(@__FILE__))
+    data_path = joinpath(main_path, "data")
+    main_image_path = joinpath(main_path, "data", "main")
+    alt_image_path = joinpath(main_path, "data", "alt")
+
+    if alt
+        # pos_testing_path = joinpath(alt_image_path, "testing", "pos")
+        # neg_testing_path = joinpath(homedir(), "Desktop", "Assorted Personal Documents", "Wallpapers copy")
+        pos_testing_path = joinpath(main_image_path, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$main_image_path/testset/faces/"
+        neg_testing_path = joinpath(main_image_path, "testset", "non-faces")
+    else
+        pos_testing_path = joinpath(main_image_path, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$main_image_path/testset/faces/"
+        neg_testing_path = joinpath(main_image_path, "testset", "non-faces")
+    end
+
+    # pos_testing_path = joinpath(data_path, "lizzie-testset", "faces")
+    # neg_testing_path = joinpath(data_path, "lizzie-testset", "nonfaces")
+
+	if ! isfile(joinpath(dirname(@__FILE__), "data", "haar-like_features"))
+		error(throw("You do not have a data file.  Ensure you run \"write.jl\" to obtain your Haar-like features before running this script/"))
+	end
+
+	# read classifiers from file
+	classifiers = deserialize(joinpath(dirname(@__FILE__), "data", "haar-like_features"))
+
+    FD.notify_user("Loading test faces...")
+
+    faces_testing = FD.load_images(pos_testing_path)[1]
+    # faces_ii_testing = map(FD.to_integral_image, faces_testing)
+    faces_ii_testing = map(FD.to_integral_image, faces_testing)
+    println("...done. ", length(faces_testing), " faces loaded.")
+
+    FD.notify_user("Loading test non-faces..")
+
+    non_faces_testing = FD.load_images(neg_testing_path)[1]
+    non_faces_ii_testing = map(FD.to_integral_image, non_faces_testing)
+    println("...done. ", length(non_faces_testing), " non-faces loaded.\n")
+
+    FD.notify_user("Testing selected classifiers...")
+    correct_faces = 0
+    correct_non_faces = 0
+
+    # correct_faces = sum([FD._get_feature_vote(face, classifiers) for face in faces_ii_testing])
+    # correct_non_faces = length(non_faces_testing) - sum([FD._get_feature_vote(nonFace, classifiers) for nonFace in non_faces_ii_testing])
+    correct_faces = sum(FD.ensemble_vote_all(faces_ii_testing, classifiers))
+    correct_non_faces = length(non_faces_testing) - sum(FD.ensemble_vote_all(non_faces_ii_testing, classifiers))
+    correct_faces_percent = (float(correct_faces) / length(faces_testing)) * 100
+    correct_non_faces_percent = (float(correct_non_faces) / length(non_faces_testing)) * 100
+
+    faces_frac = string(correct_faces, "/", length(faces_testing))
+    faces_percent = string("(", correct_faces_percent, "% of faces were recognised as faces)")
+    non_faces_frac = string(correct_non_faces, "/", length(non_faces_testing))
+    non_faces_percent = string("(", correct_non_faces_percent, "% of non-faces were identified as non-faces)")
+
+    println("...done.\n")
+    FD.notify_user("Result:\n")
+
+    @printf("%10.9s %10.15s %15s\n", "Faces:", faces_frac, faces_percent)
+    @printf("%10.9s %10.15s %15s\n\n", "Non-faces:", non_faces_frac, non_faces_percent)
+end
+
+@time main(smart_choose_feats=true, alt=false)

examples/write.jl

@@ -0,0 +1,82 @@
+#!/usr/bin/env bash
+    #=
+    exec julia --project="$(realpath $(dirname $0))/" "${BASH_SOURCE[0]}" "$@" -e "include(popfirst!(ARGS))" \
+    "${BASH_SOURCE[0]}" "$@"
+    =#
+
+
+#=
+Adapted from https://github.com/Simon-Hohberg/Viola-Jones/
+=#
+
+
+println("\033[1;34m===>\033[0;38m\033[1;38m\tLoading required libraries (it will take a moment to precompile if it is your first time doing this)...\033[0;38m")
+
+include(joinpath(dirname(dirname(@__FILE__)), "src", "FaceDetection.jl"))
+
+using .FaceDetection
+const FD = FaceDetection
+using Printf: @printf
+using Images: imresize
+using Serialization: serialize
+
+println("...done")
+
+function main(;
+    smart_choose_feats::Bool=false, alt::Bool=false
+)
+    # we assume that `smart_choose_feats = true`
+    main_path = dirname(dirname(@__FILE__))
+    data_path = joinpath(main_path, "data")
+    main_image_path = joinpath(main_path, "data", "main")
+    alt_image_path = joinpath(main_path, "data", "alt")
+
+    if alt
+        pos_training_path = joinpath(alt_image_path, "pos")
+        neg_training_path = joinpath(alt_image_path, "neg")
+    else
+        pos_training_path = joinpath(main_image_path, "trainset", "faces")
+        neg_training_path = joinpath(main_image_path, "trainset", "non-faces")
+    end
+
+    # pos_training_path = joinpath(data_path, "lfw-all")
+    # neg_training_path = joinpath(data_path, "all-non-faces")
+
+    num_classifiers = 10
+
+    min_size_img = (19, 19) # default for our test dataset
+    if smart_choose_feats
+        # For performance reasons restricting feature size
+        notify_user("Selecting best feature width and height...")
+
+        max_feature_width, max_feature_height, min_feature_height, min_feature_width, min_size_img = determine_feature_size(pos_training_path, neg_training_path)
+
+        println("...done.  Maximum feature width selected is $max_feature_width pixels; minimum feature width is $min_feature_width; maximum feature height is $max_feature_height pixels; minimum feature height is $min_feature_height.\n")
+    else
+        min_feature_height = 8
+        max_feature_height = 10
+        min_feature_width = 8
+        max_feature_width = 10
+    end
+
+
+    FD.notify_user("Loading faces...")
+
+    faces_training = FD.load_images(pos_training_path)[1]
+    faces_ii_training = map(FD.to_integral_image, faces_training) # list(map(...))
+    println("...done. ", length(faces_training), " faces loaded.")
+
+    FD.notify_user("Loading non-faces...")
+
+    non_faces_training = FD.load_images(neg_training_path)[1]
+    non_faces_ii_training = map(FD.to_integral_image, non_faces_training) # list(map(...))
+    println("...done. ", length(non_faces_training), " non-faces loaded.\n")
+
+    # classifiers are haar like features
+    classifiers = FD.learn(faces_ii_training, non_faces_ii_training, num_classifiers, min_feature_height, max_feature_height, min_feature_width, max_feature_width)
+
+	# write classifiers to file
+	serialize(joinpath(dirname(@__FILE__), "data", "haar-like_features"), classifiers)
+end
+
+@time main(smart_choose_feats=true, alt=false)